Prompt Engineering and Context Engineering

Prompt engineering definition and purpose

Prompt engineering is the process of structuring natural-language inputs (prompts) so a generative AI model produces specified outputs. It involves designing and refining the wording and structure of instructions—often by adding relevant context, specifying the desired output style, and assigning roles—to guide the model toward more accurate, relevant, and consistent results. In practice, prompt engineering may use techniques such as few-shot prompting, chain-of-thought prompting, and role assignment, and it can be applied to both text-based and multimodal models (e.g., text-to-image). The purpose of prompt engineering is to improve model performance despite the model’s sensitivity to prompt details. Research shows that small changes in phrasing, formatting, or example ordering can significantly affect outcomes, so effective prompting requires understanding how the model interprets language. Related work includes context engineering, which manages non-prompt inputs such as system instructions, retrieved knowledge, tool definitions, and metadata to improve reliability and efficiency in production systems. Prompt engineering has also expanded into automated methods (e.g., retrieval-augmented generation) and optimization approaches that search for better prompts, while also raising security concerns such as prompt injection attacks.

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Prompt terminology and what a prompt is

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